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RAPIDS Plugin For Apache Spark Integration Tests

This is a set of integration tests for the RAPIDS Plugin for Apache Spark. These tests are intended to be able to be run against any Spark-compatible cluster/release to help verify that the plugin is doing the right thing in as many cases as possible.

There are two sets of tests here. The pyspark tests are described here. The scala tests are described here

Dependencies

The tests are based off of pyspark and pytest running on Python 3. There really are only a small number of Python dependencies that you need to install for the tests. The dependencies also only need to be on the driver. You can install them on all nodes in the cluster but it is not required.

pytest

pip install pytest

Should be enough to get the basics started.

sre_yield

pip install sre_yield

sre_yield provides a set of APIs to generate string data from a regular expression.

Running

Running the tests follows the pytest conventions, the main difference is using spark-submit to launch the tests instead of pytest.

$SPARK_HOME/bin/spark-submit ./runtests.py

See pytest -h or $SPARK_HOME/bin/spark-submit ./runtests.py -h for more options.

Most clusters probably will not have the RAPIDS plugin installed in the cluster yet. If just want to verify the SQL replacement is working you will need to add the rapids-4-spark and cudf jars to your spark-submit command.

$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-0.1-SNAPSHOT.jar,cudf-0.14.jar" ./runtests.py

You don't have to enable the plugin for this to work, the test framework will do that for you.

All of the tests will run in a single application. They just enable and disable the plugin as needed.

You do need to have access to a compatible GPU with the needed CUDA drivers. The exact details of how to set this up are beyond the scope of this document, but the Spark feature for scheduling GPUs does make this very simple if you have it configured.

timezone

The RAPIDS plugin currently only supports the UTC time zone. Spark uses the default system time zone unless explicitly set otherwise. To make sure that the tests work properly you need to configure your cluster or application to run with UTC. The python framework cannot always do this for you because it risks overwriting other java options in the config. Please be sure that the following configs are set when running the tests.

  • spark.driver.extraJavaOptions should include -Duser.timezone=GMT
  • spark.executor.extraJavaOptions should include -Duser.timezone=GMT
  • spark.sql.session.timeZone=UTC

Enabling TPCxBB/TPCH/Mortgage Tests

The TPCxBB, TPCH, and Mortgage tests in this framework can be enabled by providing a couple of options:

  • TPCxBB tpcxbb-format (optional, defaults to "parquet"), and tpcxbb-path (required, path to the TPCxBB data).
  • TPCH tpch-format (optional, defaults to "parquet"), and tpch-path (required, path to the TPCH data).
  • Mortgage mortgage-format (optional, defaults to "parquet"), and mortgage-path (required, path to the Mortgage data).

As an example, here is the spark-submit command with the TPCxBB parameters:

$SPARK_HOME/bin/spark-submit --jars "rapids-4-spark_2.12-0.1-SNAPSHOT.jar,cudf-0.14.jar,rapids-4-spark-tests_2.12-0.1-SNAPSHOT.jar" ./runtests.py --tpcxbb_format="csv" --tpcxbb_path="/path/to/tpcxbb/csv"

Writing tests

There are a number of libraries provided to help someone write new tests.

data_gen.py

data_gen allow you to generate data for various spark types. There tends to be a ${Type}Gen class for every ${Type}Type class supported by the plugin. Each of these has decent default values, so a DoubleGen should produce a random set of DoubleType including most corner cases like -0.0, NaN, Inf and -Inf.

Many of the classes also allow for some customization of the data produced.

All of the classes allow you to add in your own corner cases using with_special_case. So if you want an IntegerGen that produces a lot of 5s you could run.

IntegerGen().with_special_case(5, weight=200)

The value passed in can be a constant value, or it can be a function that takes a random.Random instance to generate the data. The weight lets you set a relative priority compared with other data. By default the randomly distributed data has a weight of 100, and special cases have a weight of 1.

Not everything is documented here and you should look around at the library to see what it supports. Feel free to modify it to add in new options but be careful because we want to maintain good data coverage.

To generate data from a list of *Gen instances or from a single StructGen you can use the gen_df function.

asserts.py and marks.py

asserts.py provides 2 APIs that let you run a command and verify that it produced the "same" result on both the CPU and the GPU.

assert_gpu_and_cpu_are_equal_collect and assert_gpu_and_cpu_are_equal_iterator the difference is in how the results are brought back for verification. Most tests should use the collect version. If you have a test that will produce a very large amount of data you can use the iterator version, but it will be much slower.

Each of these take a function as input. The function will be passed an instance of spark that is configured for the given environment and it should return a data frame. It also takes a dictionary of config entries that can be set when the test runs. Most config entries you care about should be through markers.

pytest uses markers to tag different tests with metadata. This framework uses them to be able to set various configs when running the tests. Markers were chosen because it provides a lot of flexibility even with parameterized tests.

The marks you care about are all in marks.py

  • ignore_order tells the asserts to sort the resulting data because the tests may not produce the results in the same order
  • incompat tells the tests to enable incompat operators. It does not enable approximate comparisons for floating point though.
  • approximate_float tells the tests to compare floating point values (including double) and allow for an error. This follows pytest.approx and will also take rel and abs args.
  • allow_non_gpu tells the tests that not everything in the query will run on the GPU. You can tell it to allow all CPU fallback by @allow_non_gpu(any=True) you can also pass in class names that are white listed for CPU operation.

spark_session.py

For the most part you can ignore this file. It provides the underlying Spark session to operations that need it, but most tests should interact with it through asserts.py.